The study of effective connectivity (EC) is essential in understanding how the brain integrates and responds to various sensory inputs. Model-driven estimation of EC is a powerful approach that requires estimating global and local parameters of a generative model of neural activity. Insights gathered through this process can be used in various applications, such as studying neurodevelopmental disorders. However, accurately determining EC through generative models remains a significant challenge due to the complexity of brain dynamics and the inherent noise in neural recordings, e.g., in electroencephalography (EEG). Current model-driven methods to study EC are computationally complex and cannot scale to all brain regions as required by whole-brain analyses. To facilitate EC assessment, an inference algorithm must exhibit reliable prediction of parameters in the presence of noise. Further, the relationship between the model parameters and the neural recordings must be learnable. To progress toward these objectives, we benchmarked the performance of a Bi-LSTM model for parameter inference from the Jansen-Rit neural mass model (JR-NMM) simulated EEG under various noise conditions. Additionally, our study explores how the JR-NMM reacts to changes in key biological parameters (i.e., sensitivity analysis) like synaptic gains and time constants, a crucial step in understanding the connection between neural mechanisms and observed brain activity. Our results indicate that we can predict the local JR-NMM parameters from EEG, supporting the feasibility of our deep-learning-based inference approach. In future work, we plan to extend this framework to estimate local and global parameters from real EEG in clinically relevant applications.

翻译：有效连接性的研究对于理解大脑如何整合并响应各种感觉输入至关重要。基于生成模型的EC估计是一种强有力的方法，它需要估计神经活动生成模型的全局与局部参数。通过此过程获得的洞见可用于多种应用，例如研究神经发育障碍。然而，由于大脑动力学的复杂性以及神经记录（如脑电图）中固有的噪声，通过生成模型准确确定EC仍然是一个重大挑战。当前研究EC的模型驱动方法计算复杂，且无法按全脑分析所需扩展到所有脑区。为促进EC评估，推断算法必须在存在噪声的情况下对参数实现可靠预测。此外，模型参数与神经记录之间的关系必须是可学习的。为推进这些目标，我们在不同噪声条件下，对Bi-LSTM模型从Jansen-Rit神经质量模型模拟EEG中进行参数推断的性能进行了基准测试。此外，本研究通过敏感性分析探索了JR-NMM如何响应关键生物参数（如突触增益和时间常数）的变化，这是理解神经机制与观测到的大脑活动之间联系的关键步骤。我们的结果表明，可以从EEG预测局部JR-NMM参数，这支持了我们基于深度学习的推断方法的可行性。在未来的工作中，我们计划将此框架扩展到临床相关应用中，从真实EEG估计局部和全局参数。